title: Natural product-like virtual libraries: recursive atom-based enumeration.
abstract: A new molecular enumerator is described that allows chemically and architecturally diverse sets of natural product-like and drug-like structures to be generated from a core structure as simple as a single carbon atom or as complex as a polycyclic ring system. Integrated with a rudimentary machine-learning algorithm, the enumerator has the ability to assemble biased virtual libraries enriched in compounds predicted to meet target criteria. The ability to dynamically generate relatively small focused libraries in a recursive manner could reduce the computational time and infrastructure necessary to construct and manage extremely large static libraries. Depending on enumeration conditions, natural product-like structures can be produced with a wide range of heterocyclic and alicyclic ring assemblies. Because natural products represent a proven source of validated structures for identifying and designing new drug candidates, mimicking the structural and topological diversity found in nature with a dynamic set of virtual natural product-like compounds may facilitate the creation of new ideas for novel, biologically relevant lead structures in areas of uncharted chemical space.
authors.affiliation: Eisai, Inc. 4 Corporate Drive, Andover, Massachusetts 01810, United States.
authors.forename: Melvin J
authors.initials: MJ
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001688:Biological Products / Q000737:chemistry*; D055808:Drug Discovery; D015394:Molecular Structure
chemical_list: D001688:Biological Products